SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10326–10350 of 15113 papers

TitleStatusHype
Offline and Distributional Reinforcement Learning for Wireless Communications—0
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration—0
Offline Decentralized Multi-Agent Reinforcement Learning—0
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data—0
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives—0
Offline Fictitious Self-Play for Competitive Games—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
Offline Hierarchical Reinforcement Learning via Inverse Optimization—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
Offline Imitation Learning Through Graph Search and Retrieval—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Offline Inverse Reinforcement Learning—0
Offline Learning in Markov Games with General Function Approximation—0
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Offline Multitask Representation Learning for Reinforcement Learning—0
Offline Multi-task Transfer RL with Representational Penalization—0
Offline-Online Reinforcement Learning: Extending Batch and Online RL—0
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs—0
Offline Policy Evaluation and Optimization under Confounding—0
Offline Policy Optimization in RL with Variance Regularizaton—0
Offline Policy Optimization with Variance Regularization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified